RanABD: Web Page Randomization for Advanced Web-Bot Detection

In the current digital landscape, cyberattacks have become increasingly sophisticated in their attempts to evade detection. One such example is the session-replay web bot attack, where hackers use previously recorded human mouse movements (i.e., sessions) to emulate human behavior on the target websites and apps. With the emergence of advanced AI, hackers are further expected to utilize these programs to generate carefully randomized session-replay bots that still exhibit human-like behavior but without replaying/repeating identical mouse trajectories, as was previously the case. Detecting such advanced bots in the traditionally designed web pages and sites is exceptionally hard if not impossible. In this paper, we propose RanABD, a novel defensive web page randomization technique designed to counter advanced web bots. RanABD performs randomized micro modifications in the alignment of select visual HTML elements and element attributes in the target web page, while causing minimal disturbances in the page's overall appearance and functionality. By doing so, the technique ensures that the distances between trajectories of genuine human-visitors, as well as trajectories of repeat visits by the same human user, are sufficiently separated in the Feature Space. For session-replay bot operators, the only way to bypass this defence is by increasing the degree of randomization in replay sessions, but this approach is likely to backfire as it inevitably results in outlier-like trajectories that are even easier to detect. According to our knowledge, this is the first research paper that explicitly addresses the issue of advanced session-replay bots as well as proposes a novel technique that can effectively detect these specific types of bots.